Imagine Learning Highlights Linkerd's Role in Cloud-Native Scale and Cost Savings

"The case study noted that Linkerd reduced compute requirements by over 80%, cut service mesh-related CVEs by 97% in 2024, and is projected to lower regional data transfer networking costs by at least 40%. They also described Linkerd's simplicity, performance, and security as central to their decision to adopt it. According to the case study, streamlined configuration meant the mesh could be deployed reliably within hours, which shortened onboarding time for engineers and reduced the risk of downtime during rollout."

"Imagine Learning says Linkerd has become the backbone of its cloud-native infrastructure, a story the Cloud Native Computing Foundation (CNCF) has spotlighted in a new blog post. The education technology provider, which serves millions of users on Amazon EKS, described Linkerd as essential to ensuring that its platform could handle rapid growth and the demands of online learning at scale. The company highlighted improvements in reliability, scalability, and security, noting that these gains coincided with a 20% reduction in operational overhead."

"Other organisations in different sectors have described similar experiences. Hokstad Consulting reported that, in testing, Linkerd required lower CPU and memory use than Istio in their benchmarks, which they said resulted in reduced cost and overhead. This illustrated how the technology could meet the needs of platforms with large transaction volumes and availability requirements. In another example, travel company loveholidays explained that the service mesh " caught hundreds of failed deployments early, improved conversion rates by 2.61%, and reduced network costs." The company tied these improvements to both customer experience and financial results."

Imagine Learning uses Linkerd as the backbone of its cloud-native infrastructure to support millions of users on Amazon EKS and to handle rapid growth and online learning demands. Linkerd adoption produced improvements in reliability, scalability, and security while coinciding with a 20% reduction in operational overhead. Reported results include an over 80% reduction in compute requirements, a 97% cut in service mesh-related CVEs in 2024, and projected regional data transfer savings of at least 40%. Streamlined configuration enabled reliable deployment within hours, shortened engineer onboarding, and lowered rollout downtime risk. Independent benchmarks and other companies reported similar cost and performance gains.

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